Card game system with automatic bet recognition

ABSTRACT

An exemplary embodiment of the invention includes a bet recognition system for use with a card game. A table is provided for playing a card game including a bet location for each player. An image capture device is positioned in proximity to the bet location and configured to capture an image of a player&#39;s bet. An image processor is coupled to the image capture device and configured to process the image by locating at least one chip and creating a signal representing the chip, comparing the signal to a plurality of stored signatures, and when a match occurs, generating a signal representing the bet. In one aspect, the image processor is configured to generate an error signal when unable to match a candidate signature with at least one of the stored signatures.

RELATED APPLICATIONS

This application claims priority to U.S. Prov. No. 60/644,208 filed Jan.14, 2005, incorporated herein by reference.

FIELD

The invention relates to systems for monitoring casino card games andcard game players whereas the system includes a means for automatic betrecognition. In particular, the invention provides a technique foracquiring an image of a bet at particular times during the play of acard game, processing the image, and monitoring the bets made by thegame players during the play of the game. The system may further includeone or more data input or acquisition devices to input other informationsuch as the denomination and quantity of chips comprising the bet, thedenomination and quantity of chips comprising the inventory of the gametable's chip tray, to identify all positions for which game players areactively engaged in the play of the game and scan and identify the cardvalue, rank and suit and disposition of all cards dealt from the gamedeck to a game hand, a game player and/or a game dealer engaged in theplay of the game, and a means for determining the outcome, win/loss, ofeach game hand according to the game rules and so forth.

BACKGROUND

Billions of dollars are wagered annually on card games, therefore ameans to accurately monitor, record and identify the amount of each betand to subsequently determine the outcome (win/loss) of each wageraccording to the game rules is vital to the successful operation of suchcard games, as is a real time means for monitoring the inventory of thechip tray associated with the game being played. The amounts bet and thewins and the losses of the games and game players can be significant.Unscrupulous players and employees may be inclined to attempt to cheatduring play of the game and/or steal chips from the chip tray. Likewise,it is also possible that an unscrupulous dealer may cheat, or act incooperation with a player who is cheating. Casinos could savesignificant amounts of money lost to cheaters and dealer theft with asystem that could automatically detect the amount of each bet and thendetermine the outcome of each game hand, the proper win and loss foreach game hand played during a game round or playing session, and ameans for reconciling the chip inventory of the chip tray at thebeginning of a game round with the chip inventory of the chip tray atthe end of each game round in accordance with the amounts won or lost bythe players, as determined by the automatic bet recognition system,during a game round.

Conventional types of automatic bet recognition techniques are known.For example, a casino may embed radio frequency identification (RFID)tags in their chips, which are then read by an RFID reader proximate toa betting area. While this technique may be advantageous, it may resultin erroneous bets since it may not be able to accurately direct the RFIDsensing in a limited region of the table. Patents describing thistechnique include U.S. Pat. Nos. 5,735,742 and 5,651,548. Another typeof automatic bet recognition uses a camera in an attempt to image andthen determine the amount of each wager. However, conventional systemsof this type use cameras mounted relatively distant from the bettingarea or use a single camera attempting to image the entire game table,which does not provide an accurate and reliable way to determine thebet. Patents describing this technique include U.S. Pat. No. 6,758,751.

SUMMARY

What is needed is a reliable system for acquiring an image of one ormore bets associated with a particular seat, position or game player andthen processing the image(s) to accurately determine the value of thewager(s). Preferably the chip(s) comprising the bet would be stacked ina vertical position. Such a system could accurately determine the amountof each player's bet(s) and whether or not a bet was won or lost, whichwould potentially save casinos significant amounts of money. Further, areliable means for acquiring an image of the game tables chip trayinventory, and accurately calculating the number and denomination ofeach chip contained in the chip tray, and determining the total value ofthe chip tray's inventory, in real time, is also needed. The chip traywould preferably be transparent and any chip tray imaging device wouldbe capable of capturing an accurate image of chips placed horizontallyin one or more one-half cylinder shaped tubes comprising the chip tray;and whereas the tubes would be slightly angled upward, from the back ofthe chip tray closest to the dealer, toward the front of the chip traywhich would be closest to the players; and whereas the upward angle ofeach tube would cause the chips to remain flush against one anotherincreasing the accuracy of the chip tray's imaging device. Integratingthe automatic bet recognition device and the automatic chip tray imagingdevice into the system enables the system to generate a real timetabulation of the win/loss of each player, the win/loss of the gametable, and to activate an audible or visual alert when a players bet atthe end of a game round is inconsistent with the amount that the betshould be, according to the game rules, at the end of a game round;and/or activate an audible or visual alert when the inventory of thechip tray is inconsistent with the amount of chips that should be in thechip tray when the settlement of the bets for the current game round iscompleted. Further, the system includes a means for the game dealer toenter into the system the amounts of any cash or call bets that wouldnot be identified by the imaging device.

The invention overcomes a number of aforesaid limitations ofconventional systems and provides a system and method that can reliablyacquire an image of a bet and then process the image to accuratelydetermine the amount wagered. Aspects of the invention can include oneor more data input or acquisition devices to input other informationsuch cash or call bets, the denomination and quantity of chipscomprising the inventory of the game table's chip tray, debit and credittransactions (fills, chips returned to casino cage, credit issued to andpaid by game players), positions or seats occupied by players engaged inthe play of the game, cards dealt to each player hand, each playersstrategy proficiency, the outcome of each game round and so forth.

An exemplary embodiment of the invention includes a bet recognitionsystem for use with a card game. A game table is provided for playing acard game including a bet location for each player. An image capturedevice is positioned in proximity to the bet location and configured tocapture an image of a player's bet. An image processor is coupled to theimage capture device and configured to process the image by locating atleast one chip and generating a signal representing the chip, comparingthe signal to a plurality of stored signatures, and when a match occurs,generating a signal representing the bet. In one aspect, the imageprocessor is configured to generate an error signal when unable to matcha candidate signature with at least one of the stored signatures.

An exemplary embodiment of processing an image representing a pluralityof chips having a plurality of different denominations comprises thesteps of capturing an image representing the plurality of chips,identifying edges of the plurality of chips, segmenting the image into aplurality of individual candidate chips, generating a signature for eachof the candidate chips, identifying each of the candidate chips bycomparing the signature of each candidate chip to a plurality of storedsignatures representing valid chips and associated denominations, andadding denominations associated with each of the valid chips todetermine the wager.

An exemplary embodiment of training an image processor to process animage representing a plurality of chips having a plurality of differentdenominations comprises the steps of capturing a plurality of imagesrepresenting chips having different denominations, generating asignature for each of the chips having different denominations, andstoring the signatures.

The invention provides numerous aspects and advantages to a card gamewith automatic bet recognition. Advantages of the invention include theability to identify chips wagered by a player to automatically determinethe player's bet for a game hand and the player's win or loss during aplaying session. The invention can also track bets during the course ofa game to automatically determine the player's betting strategy relativeto one or more card count systems programmed into the system.

An exemplary embodiment of the invention includes a chip tray inventoryrecognition system for use with a card game. A game table is providedfor playing a card game including a chip tray to contain the gametable's bankroll. An image capture device positioned in proximity to thechip tray and configured to capture an image of the chips comprising theinventory of the chip tray. An image processor coupled to the imagecapture device and configured to process the image by locating at leastone chip and creating a signal representing the chip, comparing thesignal to a plurality of stored signatures, when a match occurs,generating a signal representing the bet. In one aspect, the imageprocessor is configured to generate an error signal when unable to matcha candidate signature with at least one of the stored signatures.

An exemplary embodiment of processing an image representing a pluralityof chips in a chip tray having a plurality of different denominationscomprises the steps of capturing an image representing the plurality ofchips, identifying edges of the plurality of chips, segmenting the imageinto a plurality of individual candidate chips, generating a signaturefor each of the candidate chips, identifying each of the candidate chipsby comparing the signature of each candidate chip to a plurality ofstored signatures representing valid chips and associated denominations,and adding denominations associated with each of the valid chips todetermine the amount of the chip tray inventory.

An exemplary embodiment of training an image processor to process animage representing a plurality of chips in a chip tray having aplurality of different denominations comprises the steps of capturing aplurality of images representing chips having different denominations,generating a signature for each of the chips having differentdenominations, and storing the signatures.

The invention provides numerous aspects and advantages to a card gamewith automatic chip tray inventory recognition. Advantages of theinvention include the ability to identify the chip tray inventory duringreal time and provide a running count of the chip tray's inventory andto automatically determine the game tables win or loss during the playof the game; and/or to automatically determine the win or loss of aplurality of game tables coupled to the system during real time.

DRAWINGS

These and other features and advantages will become better understoodwith reference to the description, claims and drawings.

FIGS. 1A-B depict a gaming table system including peripheral components,a computer, memory and peripheral interfaces according to an embodimentof the invention.

FIG. 2 is a flowchart depicting a method according to an embodiment ofthe invention.

FIG. 3 is a digital image of two stacks of gaming chips on a table,acquired with a conventional digital camera, at a resolution of960×1280×3 bytes (24-bit color).

FIGS. 4A-B are exemplary flowcharts showing methods to process digitalimages of chips according to embodiments of the invention.

FIG. 5 depicts intersecting lines of vertical and horizontal edgesdetected in the image of FIG. 3 following flowchart 450 step 468.

FIG. 6 depicts a final segmentation of the image of FIG. 3, afterflowchart 450 step 472. Grey areas are filled regions identified aspossible chip stacks. White lines are borders of the potential chipstacks, as determined by the method.

FIG. 7 depicts images of the three identified potential chip stacks fromFIG. 3, after the completion of the method described flowcharts 400 and450. The first candidate stack is an edge of a soup can and will beeliminated from consideration in subsequent steps of the method.

FIG. 8 is an exemplary flowchart showing an exemplary method to segmentan image of a stack of chips into individual chip images.

FIG. 9A depicts an original digital image of an isolated single stack ofchips, and the output of intermediate stages of the method shown in FIG.8. FIG. 9B depicts the results of horizontal edge detection. FIG. 9Cshows the most likely interchip boundaries determined by the method.

FIG. 10 depicts images of several individual chips from the stack ofFIG. 9, identified in the chip location step.

FIG. 11 is an exemplary flowchart showing a method to locate color bandsin the image of an individual gaming chip, and generate a list of bandcolors and widths for input to a chip identification method.

FIG. 12 depicts an original digital image of a single gaming chip,including some background areas at the edges of the chip, which is theoutput of an exemplary method. FIG. 12A depicts the original digitalimage. FIG. 12B shows the edge boundaries of vertically divided regionsof the chip edge, as determined by the method. The smooth black linemarks the 3× s.d. of the background, a recommended threshold fordetermining which peaks are significant. FIG. 12C shows the observedcolors of the bands of the chip in 12A, after merging adjacent regionsand excluding edge regions. FIG. 12D shows the bands after brightnessnormalization. The four color bands depicted will be input to the neuralnetwork classifier of the method.

FIG. 13 depicts a standard signature pattern of the example chip of FIG.12, as computed in the exemplary method.

FIG. 14 depicts an exemplary architecture for a radial basis functionneural network, suitable for matching lists of transformed colors andpatterns to those expected for authorized chip patterns. The input layerof the network includes nodes for each entry in the standard signaturepattern of any chip. In this example, the chip signatures include thenormalized red and green intensities, and the radial width, of up tofour color bands. More bands, and the sizes and colors of enclosedsymbols, are included in the signature pattern in other embodiments. TheRBF layer comprises two-dimensional Gaussian functions, which arecentered at the positions of the various standardized colors ofauthorized chips. Each input band (three node group) is connected toeach RBF node. The RBF nodes are activated proportionately to theprobability that the color represented by the node is present among theinput bands. The output layer is trained to report the probability thatthe input band colors and widths match the pattern of an authorized chipdenomination. Each output node reports the probability that the inputchip is a chip of the denomination represented by that node. The valueof the output node is the sum of its inputs, each weighted by a weightassociated with each line in FIG. 14, added to a “bias” constant, andoperated upon by a “transfer function.” Various linear and nonlineartransfer functions are commonly employed. The preferred embodiment useda sigmoid transfer function (tanh or equivalent), which allows theoutput values to be interpreted as probabilities.

FIG. 15 depicts the radial basis functions of the second layer of a RBFneural network trained to recognize 15 distinct colors. The number ofdistinct colors recognized in any particular embodiment will depend onthe variety of authorized chip patterns used to train the network, astaught in the method. As clarified from FIG. 15, 15 distinct colors arereadily distinguished by this method.

FIG. 16 depicts the output of the neural network depicted in FIG. 14,when presented as input with the standard signature patterns of some ofthe chip images of FIG. 10. Chips 5, 15, and 18, counting from the topof the stack, are chips of authorized denomination $5, and their outputsare shown as blue bars. Chips 19 and 21 in the stack of FIG. 8 are chipsof authorized denomination $1000, shown as green bars. Chips 1 and 3 ofthe stack of FIG. 8 are unauthorized chips, shown as red bars. Theneural network correctly classifies all of the chips.

DETAILED DESCRIPTION

The invention is described with reference to specific apparatus andembodiments. Those skilled in the art will recognize that thedescription is for illustration and to provide the best mode ofpracticing the invention. As referred to herein, a game constitutes oneor more hands of cards.

A. Card Game System

FIG. 1A depicts a gaming table system 100 according to an exemplaryembodiment of the invention. The table includes a card area 102 forplacement of the cards during play of the game, and a betting area 104for the player to place a wager. FIG. 1B is an overhead view of thetable system 100 showing the player positions 130 a-e, the card area 102and the respective betting areas 104 a-e for the player stations.

The dealer station includes a card shoe 106 for storing the cards thatthe dealer deals to each of the players. The exemplary card shoeincludes a card scanner of the type described in U.S. Pat. Nos.5,362,053; 5,374,061; 5,722,893; 6,039,650; 6,299,536 and 6,582,301 allincorporated herein by reference in their entirety. The card scanner isconnected to an interface 108 that provides a signal to a computer 110,which determines the cards dealt to the players.

The dealer station also includes a chip tray 120 where the dealer storeschips for paying out players when they win and for collecting their betswhen they lose. In one aspect of the invention, the chip tray istransparent and a camera 122 is positioned under the chip tray tocapture images of the chips in the chip tray 120 during the course ofthe game, including before and after each round. A light can be includedunder the tray to provide illumination to the chips. The chip traycamera 122 is connected to an interface 128 that provides informationregarding the chips stored in the tray to the computer 110. In anexemplary embodiment, the chip tray camera periodically scans the chiptray and communicates images of the contents of the chip tray to thecomputer, which can then determine the content of the chip trap byperforming image processing as described herein. In one aspect, dealersare issued dealer tracking cards and a dealer tracking card (DTC) reader126 is coupled to the processor and configured to read dealer trackingcards issued by a casino having information regarding the dealers. Inthis manner, the invention can track the contents of the chip trayduring each dealer's shift and ensure that the wins and losses arecorrectly paid into and out of the chip tray.

The player station 130 can include an exemplary display to provideplayer information including betting and game information, casinoinformation and other information via interface 132. A player trackingcard (PTC) reader 134 is provided for the player to enter a card andprovide betting information to the casino computer for gambling creditsand so forth. PTC readers are known in the art and include magnetic,optical and radio frequency identification type systems. A camera 140 isprovided in the player station to capture an image of the player's wagerin the betting area 104. Also, since a casino may have inconsistentlighting, in one aspect, a light 144 is provided to illuminate the wagerin betting area 104. The light provides a consistent image illuminationthat improves image capture and image processing in some circumstances.

An optional separate camera 148 can also be coupled to system. When thesystem determines that one or more predetermined criteria relative tothe play of the game or the game player has been achieved the camera 148is programmed to automatically focus in on the play area or seatoccupied by the game player and photograph the subject player. Theplayer's photo will be automatically transmitted and stored within acommercially available biometric database maintained by the host casinoand activate an audible or visible alert for the benefit of the dealerand or management. The predetermined criteria may include, withoutlimitation, a VIP player, a known card cheat, a known card counter or aplayer who has been barred from play has logged in as playing at thespecific game table in a specific seat, or a player who the system hasdetermined, during the play of the game, to be a highly skilled cardcounter, is varying his/her bets according to the decks running or truecount, winning an unusual amount of money or hands and so forth.

The computer 110 includes a memory 112 that stores information includingcontrol procedures 112 a, communication procedures 112 b and data 112 c.The computer 100 is also coupled to a casino computer 150, whichcollects information regarding the bets and keeps track of the money inthe casino. Operation of the computer is described below with referenceto the card game system 100.

FIG. 2 is a flowchart 200 depicting a method according to an embodimentof the invention. In step 202, the game begins and the inventionperforms wager tracking. The invention provides a means for signalingthe beginning of a game. In one aspect, the dealer presses a button onthe card shoe 106, chip tray 120 or other location to signal the startof the game to the computer. Camera 140 acquires a first image of thebet (initial wager) in the area 104 and the computer stores the image.In one aspect, the computer also processes the image as described below.In step 204, the game is played. In step 206, the game is finished andthe camera acquires a second image of the bet (final wager). In step208, the first image and second image are compared to one another. Ifthe player wins and the bets match, then the player is paid in step 212.If the house wins and the bets match, then the house keeps the wager instep 214. In either event, if step 208 determines that the final wagerdoes not match the initial wager, then step 216 generates an alarmsignal to alert the dealer and/or additional casino personnel to reviewthe game for possible fraud. In aspects of the invention, the alarm canbe an audible or visual alert in proximity to the game table and/or analert on a remote monitor in a remote location.

In one aspect, the first image is taken at the beginning of a game andthe second image is taken at the end of the game. In other aspects,images may be taken at specific times during the course of play or atperiodic time intervals during the course of play. One benefit toacquiring multiple images is that there may be times during the gamethat a change in the bet is allowed, for example in blackjack, includingdoubling down or splitting. In these circumstances, the bet may changeduring the game and additional images acquired during the game may beprocessed to ensure the lawful play of the game. In one aspect, FIG. 2includes the additional step 207 to depict intermediate imageacquisitions of the player's station during course of the game, and step207A to depict intermediate image acquisition of the dealer's chip trayduring course of the game.

In one aspect, a first image and second image are compared to oneanother to ensure that no illegal change was made to the bet during thecourse of the game. One technique for comparison is to permit a certainnumber of pixel differences between the images that define an allowedmatch, where a number of different pixels above that threshold number isdefined as a mismatch.

In yet another aspect, steps 202A and 220 are implemented to acquire animage of the chip tray and to identify the contents of the chip tray.This can be done in terms of contents or value of all the chips in thetray. Once the game is complete and the invention knows the value of thewagers won and lost by the players, the invention can calculate anexpected value of the chips that should be in the chip tray. Step 220compares the actual amount to the expected amount. If there's a mismatchbetween these amounts, the invention can alert the dealer or supervisorto investigate.

B. Automatic Bet Recognition Image Processing

An exemplary method of performing an automatic bet recognition techniqueis described below with reference to the figures. Headings 1 through 8describe exemplary aspects of the technique for the sake of describingthe invention in an organized manner and are not intended to belimiting.

1. Obtain Suitable Digital Image

FIG. 3 depicts chips placed in a betting area similar to area 104. Apreferred input to the automatic bet recognition system (ABRS) imageprocessing and pattern recognition (IPPR) system is a medium or highresolution color digital image of the betting area 104. Color resolutionshould include at least 4 bits of resolution, and preferably at least 8bits of resolution, in each color channel. The spatial resolution can becomparable to that achieved by commercially available CCD or CMOSimaging devices commonly used in digital cameras (e.g. 1 mega-pixelimage or higher). The imaging device should be aimed and focused suchthat all stacks of chips are viewed from the side and in focus. A stackof chips will therefore appear in the image as a rectangle with verticaland horizontal sides, whose width is constant and determined by thestandard diameter of a gaming chip (and the distance between the stackand the imaging device), and whose height varies depending on the numberof chips in the stack.

In some embodiments of the invention, the value of chips in a chip trayis to be imaged and evaluated. In these embodiments, the stacks of chipswill be horizontal rather than vertical, and will be confined to aconstrained area (i.e. the chip tray). In the following discussion,references to “vertical” and “horizontal” dimensions of chip stacksshould be understood to be reversed in such embodiments. Also, certainfunctions, e.g., function 2 describing location of stacks of chips inthe image, may be obviated in such embodiments.

All stacks of chips to be evaluated are preferably completely containedwithin the image, and any extraneous objects (e.g. cards, water bottles,etc.) in the image should be spatially disjoint from the stacks ofchips. If more than one stack of chips is present in the betting area,each such stack should be disjoint from and not occlude any other suchstacks in the image. The lighting should be as uniform as possible,without distinct shadows superimposed on the stack. The backgroundshould contrast as much as possible with the colors of the chips. Suchcontrast is facilitated if the imaging device is focused at the distanceof the betting area, so that distant backgrounds, e.g. players'clothing, is somewhat out of focus.

The exemplary card gaming system is shown with reference to theexemplary embodiment shown in FIG. 1. Details of the exemplary physicalembodiment of the imaging hardware used to obtain the required images,its spatial orientation on the game table, and the external signalsand/or internal timers used to trigger the acquisition of an image, arecomponents of the ABRS system which employs the method taught in thisinvention, and may be modified with respect to the ABRS-IPPR. Wheneversuch an image is obtained, the ABRS-IPPR will proceed to process andevaluate the stacks of chips (if any) within the betting area, using themethod taught in this invention. The ABR-IPPR may be used with otherconfigurations than shown in the exemplary embodiment of FIG. 1.

2. Locate Stacks of Chips in the Image

Once a suitable digital image has been obtained and presented to thecomputer 110 executing the ABRS-IPPR software program, the program firstlocates the stacks of chips (if any).

Initially, it is beneficial to perform some pre-processing with variousdigital filters, such as a median filter in order to normalize signalintensity, improve contrast, remove image features much smaller thangaming chips, and filter out other noise.

The invention employs a method of digital image processing. One suchmethod is called “edge detection” and searches for vertical andhorizontal lines bordering regions of different color. Another methodcalled “line continuity search” extends horizontal and vertical lines todelimit regions. A complementary method called “regional continuitysearch” seeks to find regions of consistent color and/or texture withinthe image, which are candidates for the edge surfaces of individualchips. An alternative method called “template matching” seeks to locateareas of the image that correspond to a preset template, which in thiscase is a rectangle of known width and variable height and color. Thelatter method may be performed on the original image, or,preferentially, on a two-dimensional Fourier transform of the image.While these techniques are described with reference to color, they alsowork well on a grayscale copy of the original color image.

FIG. 4A depicts a high-level method employed by the invention toidentify a player's bet, locate the candidate chips constituting thebet, match signatures of the candidate chips to those stored in memoryand then validate a match, if possible. Although many algorithmicmethods and combinations of algorithmic methods could be employed tolocate stacks of chips in an image, the preferred embodiment compriseseleven sequential steps shown in FIG. 4B.

Step 452—Vertical edge detection: Each pixel in the image is replaced bya grayscale value computed from the average difference between the colorvalues of a number of pixels to its left and the color values of anumber of pixels to its right. The appropriate number of pixels to usefor edge detection depends on the optical and digital resolution of theimage; it ranges between one pixel and about five pixels. For theexample in FIG. 3, three pixels were used for vertical edge detection.

Step 454—Vertical median filters: Each pixel in the vertical edge imageafter step 452 is replaced by the median of a vertical column of Npixels, of which it is the center. The effect of this filter is toeliminate vertical lines shorter than N/2, and to fill in gaps in longervertical lines. Appropriate values of N depend on the image resolutionand on the size of vertical boundaries to be detected—values of N from 3to ½ of the chip height may be optimal. For the example in FIG. 3, inwhich the average height of a single chip image is 30 pixels, 9 pixelswere used for the vertical median filter.

Step 456—Vertical line detection: Within a sliding rectangular window,the brightest pixel in each row is displaced into the column containingthe majority of brightest pixels, and each other pixel in the row is setto zero. The effect of this filter is to enhance vertical lines, and toconvert the grayscale image into a binary image of candidate verticaledges. The resolution and contrast of real vertical edges improves,although some random noise begins to look like vertical lines. Variousrectangle sizes can be used in this step. For the example in FIG. 3, arectangular window of height 30 pixels (the average height of a chip,hence the minimum length of real vertical edges) and of width 5 pixels(the average error of the vertical edge detector) was used. In a higherresolution or more tightly focused image, larger rectangles would beused. In a lower resolution or more distant image, smaller rectangleswould be used. The size of the optimal line detection rectangle is fixedby the properties of the imaging hardware, and remains substantiallyconstant for a particular embodiment of the invention.

Step 458—Vertical path discovery: Locate continuous vertical paths thatare at least as long as the height of a chip (30 pixels, for the examplein FIG. 3), and which may connect with other vertical paths within ahorizontal distance determined by the expected maximum misalignment ofchips in a stack (30 pixels, for the example in FIG. 3). Pixels whichare part of such vertical paths are set to binary 1's, and pixels whichare not are set to binary 0's.

Step 460—Horizontal edge detection: Each pixel in the original colorimage is replaced by a grayscale value computed from the averagedifference between the color values of a number of pixels above it andthe color values of a number of pixels below it. For the example in FIG.3, three pixels were used for horizontal edge detection.

Step 462—Horizontal median filters: Each pixel in the horizontal edgeimage after step (e) is replaced by the median of a horizontal row of Npixels, of which it is the center. The effect of this filter is toeliminate horizontal lines shorter than N/2, and to fill in gaps inlonger horizontal lines. Appropriate values of N depend on the imageresolution and on the size of horizontal boundaries to bedetected—values of N from 3 to ½ of the chip width may be optimal. Forthe example in FIG. 3, 11 pixels were used for the horizontal medianfilter. This is the horizontal distance in the image over which there isno appreciable curvature of the chip edge.

Step 464—Horizontal line detection: Within a sliding rectangular window,the brightest pixel in each column is displaced into the row containingthe majority of brightest pixels, and each other pixel in the column isset to zero. The effect of this filter is to enhance horizontal lines,and to convert the grayscale image into a binary image of candidatehorizontal edges. The resolution and contrast of real horizontal edgesimproves, although some random noise begins to look like horizontallines. Various rectangle sizes can be used in this step. For the examplein FIG. 3, a rectangular window of height 5 pixels (the average error ofthe horizontal edge detector) and of width 340 pixels (the average widthof a chip, hence the maximum length of real horizontal edges) was used.In a higher resolution or more tightly focused image, larger rectangleswould be used. In a lower resolution or more distant image, smallerrectangles would be used. The size of the optimal line detectionrectangle is fixed by the properties of the imaging hardware, andremains substantially constant for a particular embodiment of theinvention.

Step 466—Horizontal path discovery: Locate continuous horizontal pathsthat are at least as long as the uncurved width of a chip (100 pixels,for the example in FIG. 3), and which may connect with other horizontalpaths within a vertical distance determined by the expected maximumcurvature of chip edges (I pixel/100 pixels, for the example in FIG. 3).Pixels which are part of such horizontal paths are set to binary 1's,and pixels which are not are set to binary 0's.

Step 468—Boundary detection: The vertical boundary lines from step 468are combined with the horizontal boundary lines from step 466, to searchfor candidates for fully bounded rectangular regions. Vertical linesextending beyond their intersections with horizontal lines aretruncated. Likewise, horizontal lines are truncated at corners. Theresult of processing the image of FIG. 3, after boundary detection, isshown in FIG. 5.

Step 470—Flood fill regions: Enclosed regions after step 468 are floodfilled. Filled regions with dimensions less than ½ of a nominal chipdimension (height 30 pixels, width 320 pixels, in the image of FIG. 1)are eliminated.

Step 472—Clip stack candidates: The bounding rectangle of each filledregion from step 470 is a candidate for a stack of chips. The fivecandidate stacks identified in the FIG. 3 image are shown in FIG. 6. Thetwo smaller regions are rejected as too small to be chip stacks, leavingthree candidate stacks located, as shown in FIG. 7.

After stack location, the original image has been replaced by zero ormore smaller images, each including only a subset of the original imagethat depicts a stack of chips, as shown in FIG. 7. If no stacks of chipsare found, the method terminates and reports that it was unable todetect any chips in the betting area. This might happen because of afailure of the imaging hardware, a failure of the stack location method,or improper placement of a stack by the player, or it might be a normalexpected condition at the current stage of the game. In any case, theABRS system in which the ABRS-IPPR software program is embedded takesappropriate action to either alarm the dealer or casino personnel, orperhaps to identify that player position as uninhabited.

It will be readily understood by those skilled in the art that there aremany other combinations of digital filters, method steps, andpattern-matching steps known to the art which could be employed toachieve essentially the same stack location result as the exemplarysequence of digital filters and method steps described above anddepicted in FIG. 4A-B. It is understood that the present inventionincludes any and all such combinations of filters known to the art, andis not limited to the exemplary sequence of filters described above.

If the stack location method detects one or more stacks of chips,subsequent steps in the method are typically performed separately andsequentially on each such stack. The values of all the chips in eachstack are combined together for the final bet tabulation of step 410.

3. Locate Individual Chips in a Stack of Chips

The input to this step of the exemplary method is an image of a singlestack of candidate chips, or one of the plurality of stacks, as shown inFIG. 7. The next task is to divide the image into separate images, eachone an image of the edge of a single chip in the stack.

Although many algorithmic methods and combinations of algorithmicmethods could be employed to locate stacks of chips in an image, thepreferred embodiment comprises six sequential steps as shown in the FIG.8 flowchart 800.

Step 802—As for the stack location method, a horizontal line search forparallel horizontal boundaries spaced close to the known chip thickness,combined with a regional continuity search, can find many chipboundaries.

Step 804—As for the stack location method, a horizontal median filteraccentuates horizontal lines in the image. The initial separated imageof an exemplary stack of chips is shown in FIG. 9A, and the result ofthe horizontal edge filter and edge enhancement is shown in FIG. 9B.

Step 806—The average edge intensity in each row is determined, and apeak-finding method is used to locate the average vertical position ofhorizontal edges. Frequently, there will be no clear boundary betweentwo adjacent chips of the same denomination. In this case, the methoduses its knowledge of the chip thickness (or aspect ratio) to deducethat two chips are stacked rather than just one. In this case, an edgebetween two peaks of approximately double (or triple, etc) the expectedchip height is inferred. It is unnecessary that the software identify aprecise boundary between adjacent identical chips—it is sufficient toknow how many there are. In the example shown in FIG. 9A, the expectedchip height is 30 pixels, so detected “peaks” less than 25 pixels apart,caused by shadows in the image (faint diagonal lines crossing chips inFIG. 9B), are ignored.

Step 808—Starting at the average vertical position of each inferred chipboundary, a dynamic programming algorithm, known as a Viterbi algorithm,is employed to determine the most likely path of the boundary betweenadjacent chips. The chip boundaries determined by this method for thechip stack image of FIG. 9B are shown in FIG. 9C.

Step 810—The chip boundaries detected in step 808 are curved, especiallyfor chips near the top and bottom of the stack, because of parallax ofimages acquired at close range. In order to avoid including portions ofneighboring chips in the individual chip images, the chips discovered instep 808 are clipped vertically, at positions such that about 80% of thehorizontal boundary of the chip is excluded from the chip image.

Step 812—The boundaries of individual chips in the image, isolated insteps 802-810, are recorded for input to the chip identification method.

It is usually unnecessary for the chip location method to use anyknowledge about the precise colors and/or patterns of the valid chipdenominations. However, if the ambient lighting is especially uneven, orthe chip patterns of different denominations of chips are insufficientlydistinctive, the use of expected chip colors and patterns may increasethe robustness of chip location.

The chip location method may be unable to subdivide the stack image intoindividual chip images, either because the stack was misidentified (e.g.it is really a similar-looking foreign object), or the stack is poorlyaligned or contains foreign objects, or the ambient lighting or shadowobscures the chip boundaries. In some such cases, the “stack” can berejected as a foreign object, and processing can proceed on otherdetected stacks. In the exceptional case in which a stack can neither beproperly segmented nor rejected, the ABRS-IPPR notifies the parent ABRSsystem that it is unable to evaluate the bet, so the dealer can make amanual evaluation of the bet.

The final result of applying the method of flowchart 800 to the stack ofFIG. 9A, is the set of individual chip images shown in FIG. 10.

It will be readily understood by those skilled in the art that there aremany other combinations of digital filters, algorithmic steps, andpattern-matching steps known to the art which could be employed toachieve essentially the same chip location result as the exemplarysequence of digital filters and algorithmic steps described above anddepicted in FIG. 8. It is understood that the present invention includesany and all such combinations of filters known to the art, and is notlimited to the exemplary sequence of filters and algorithmic stepsdescribed above.

If the chip location method detects one or more chips in the stack,subsequent steps in the method can be performed separately andsequentially on each such chip. The values of all the chips in the stackare combined together, and with the counts and values obtained fromother stacks, for the final bet report.

4. Tabulate Vertical Bands of a Single Chip

The input to this step of the method is an image of the edge of a singlegaming chip, as shown in FIG. 12A. The next task is to characterize thecolors, widths, and patterns of the several distinct regions of the chipedge. In general, each denomination of chip that can be legitimatelywagered on a particular table has a unique combination of at least twodistinct colors, arranged periodically around the periphery of the chipsuch that, regardless of the orientation of the chip, at least onecomplete instance of the alternating pattern of colors is visible in theimage (which captures ½ of the periphery of the chip). In addition toalternating bands of different colors, the edge of the chip may displaygeometric patterns such as dots, bars, or other figures of one color,surrounded by regions of another color.

The band tabulation method characterizes each band of the chip visiblein the image. A band consists of a roughly rectangular region of theimage, spanning the full thickness of the chip, and demarcated on theleft and/or right by regions of contrasting color. In some chippatterns, the boundary between bands is not vertical but V-shaped,slanted, or irregular. The method treats such band boundaries as if theywere vertical. A flowchart of a preferred embodiment of the bandtabulation method is shown in FIG. 11.

A preferred embodiment of the vertical band tabulation step comprisesthe following steps, as depicted in FIG. 11 flowchart 1100:

Step 1102—Vertical edge detection: As for the stack location method, avertical line search finds candidate band boundaries.

Step 1102—Peak-picking: The average edge intensity in each column isdetermined, and a peak-finding method is used to locate the averagehorizontal position of vertical edges. The output of such an method isshown in FIG. 12B. Some filtering is applied to eliminate very smallregions between “peaks.” The surviving peaks delimit regions of distinctcolor, which are candidates for the distinctive color bands defining thedenomination of the chip.

Step 1102—Find the average color of each vertical band: For each region,compute the mean color, in each RGB channel, of the pixels within theregion, excluding boundary pixels.

Step 1102—In some gaming chip designs, the color bands include dots,crosses, or other symbols of contrasting color. The ABRS-IPPR system canlearn which chips have such enclosed symbols during training, asdescribed below. However, unexpected enclosed symbols may beencountered, if unauthorized chips are present. The preferred method candetect such enclosed symbols using a boundary detection and flood fillmethod similar to that described in step 470 above. Enclosed symbols arecounted and recorded for use in subsequent chip identification steps.Any such enclosed symbols are treated as separate regions in subsequentsteps—that is, the average color and relative image area of eachenclosed region is calculated. However, pixels in enclosed regions areexcluded from the average color determination of step 1102.

Step 1102—Merge adjacent regions of similar color: If two adjacentregions have similar colors, they are presumed to differ by theintervention of a shadow, and should be merged into a single region.Bands surviving this step are shown in FIG. 12C.

Step 1102—Normalize brightness: Because the incident light falling oneach region is variable, the relative intensity in the three colors ismore distinctive than the absolute intensity of any color. This isaccounted for by normalizing the total luminance of each pixel. This isdone by normalizing the red (R) and green (G) values of each band suchthat the total luminance is 1.0. This normalization converts both blackand white bands to grey; it is equivalent to removing the intensitydimension of an intensity/hue/saturation (ihs) color representation.

Step 1102—Find edges of the chip: The input images to flowchart 1100 arebounded by areas of background, which have nothing to do with the chipidentity. Regions at the extreme left and right of the chip image arelikely to consist of such irrelevant background. Color regions at theextreme left and right of the chip image are eliminated, unless they arelarge enough to extend beyond the possible boundary region.

Step 1102—Compute region size: Of the surviving regions, the true size(measured as degrees of arc) of each region is computed, based on theviewing angle. Thus, regions near the edge of the chip span fewer pixelsin the image than do similar-sized regions near the center of the image.A simple approximation assuming an infinite viewing distance is used tonormalize the region size.

Step 1102—The first and last regions identified by the above steps,unless they exceed a parameterized value, are excluded from thefollowing analysis as unreliable data. The final normalized bands in theexample chip of FIG. 12A are shown in FIG. 12D.

It will be readily understood by those skilled in the art that there aremany other combinations of digital and heuristic filters, andalgorithmic steps known to the art, which could be employed to achieveessentially the same band identification result as the exemplarysequence of digital filters and algorithmic steps described above anddepicted in FIG. 11. It is understood that the present inventionincludes any and all such combinations of filters and algorithmic stepsknown to the art, and is not limited to the exemplary sequence offilters and algorithmic steps described above.

The output of flowchart 1100 is a list of all bands in the chip image,each characterized by (a) its true radial width, (b) its averagenormalized color, (c) the number, color, and relative size of anyenclosed regions.

5. Tabulate Unique Bands

The input to this step of the method is a list of all bands detected inthe chip image, their normalized colors, and the size, number, and colorof enclosed symbols, if any. In this aspect of the method, the list ofobserved bands is reduced to a minimal ordered list of bands. The methodsearches for repeating patterns of sequences of bands of approximatelythe same color, width, and included regions. This minimal ordered listof bands constitutes the raw signature pattern of the chip.

If there are multiple examples of bands which are similar in color andsize, which are consolidated into a single band in the above step, thenthese bands are retained in memory as potentially distinct bands. Thesepotential additional bands may be used in subsequent steps of the methodto allow matches with signatures containing additional bands.

If there are included symbols within the regions, then the number,colors, and relative size of such symbols is included in the signaturepattern.

6. Standardize Colors

The input to this step of the method is an ordered list of all uniquebands detected in the chip image. Before attempting to match thissignature pattern to one of the expected authorized chip signatures, thecolors are transformed into a maximally distinctive color space. Theparameters of the color transformation are determined when the signaturepatterns of all authorized denominations of chips used at the gamingtable are specified during training, as described below. After colortransformation, the ordered list of unique band widths, transformedcolors, and the number, color, and size of included regions, constitutesthe standard signature pattern of the chip.

The standard signature pattern of the chip imaged in FIG. 10 a is shownas a color image and in the table in FIG. 13.

It should be noted that the chip depicted in FIG. 12 in fact has threedistinct color bands. The first band detected (“Orange2”, [0.74, 0.1,0.14]) is actually the same as the fourth band ((“Orange2”, [0.61, 0.18,0.10]). The standard signature patterns of these bands, actuallyidentical, are measured as different because the first is lying in theshadow of other chips in the stack. Such variations of measuredsignatures under different conditions of light and shadow is to beexpected. The exemplary method can nonetheless correctly identify gamingchips despite such variations, provided that the variation in lightingis not too severe, and the color patterns of authorized chips aresufficiently distinctive.

7. Match Chip Signature to Best Authorized Chip Signature

Once the standard signature pattern of each gaming chip has beendetermined, it is then classified as either one of the authorized chippatterns, or as an unauthorized pattern. Any of a several classificationmethods may be employed to achieve this classification, including linearclassifiers, Bayesian classifiers, hierarchical classifiers, neuralnetwork classifiers, and others. While any of these or otherclassification methods could be used, a preferred method is a radialbasis function neural network classifier. The advantage of a radialbasis function neural network classifier over many other classifiers isthat it is able to determine when a chip is not a member of theauthorized set, without having to be explicitly trained to recognizeunauthorized chips.

The standard signature pattern of the chip is supplied as input to aneural network, which computes the probability that the chip matches anauthorized chip on which the network has been trained, as explainedbelow. An example of a neural network suitable for such a determinationis provided in FIG. 14. It should be readily apparent to one skilled inthe art that there are many other neural network architectures and otherclassification methods that could be used to obtain substantiallyequivalent results to those obtained from the neural network of FIG. 14.

The neural network depicted in FIG. 14 comprises three layers: an inputlayer, a “hidden” layer, and an output layer. Each node in each layer isconnected with each other node in the layer below it. In general, thelinks between a layer and the next layer are associated with a “weight,”which is a variable parameter multiplying the value of the node in thefirst layer. Each node computes a “transfer function,” a single valuedfunction of the weighted inputs to the node, plus an additional added“bias” associated with the node. The value of the transfer function, forspecified inputs, is then propagated on to the next layer of thenetwork.

A “radial basis function” neural network is one in which the nodes ofthe second, “hidden”, layer, are peaked functions (for example, Gaussianfunctions) centered at a position in the input domain specific to thatnode (in this example, in the three-dimensional space of red, green, andchip width). With each node is associated a center and a width, and thevalue of the node is the sum of the values of the peaked transferfunction when applied to the inputs. A preferred embodiment of themethod uses radial basis functions which are three-dimensional Gaussianfunctions of the red and green values, and the radian dimensions, ofeach of the color bands represented in the input layer. With each nodeof the RBF layer there are associated six parameters: the red, green,and width coordinates of the center of the Gaussian function, and thered, green, and width standard deviations of the function. In thisexample, the standard deviations of the RBF functions in the inputdimensions were constrained to be equal, so that there are fourindependent trainable parameters associated with each node in the hiddenlayer. The red and green dimensions of the radial basis functions in thenetwork of FIG. 14, after training on the input denominationsrepresented in the stack of gaming chips shown in FIG. 3, are shown inFIG. 15. Each peak in the figure is centered at the (red, green)standardized coordinates of one of the colors of one of the bands of theauthorized chips. The value of the corresponding node of the network,when presented as input with the standardized chip signature of a chip,is the height of the RBF at the red and green coordinates observed forany color band of that chip.

The values of the output nodes (the third layer of the network depictedin FIG. 14) are computed as a weighted sum of the values of the nodes ofthe RBF layer, transformed by the transfer function of the third layer.In the preferred embodiment, the weights of the links between layers 2and 3 are trained, and the layer 3 transfer function is a sigmoidtransfer function (e.g. tanho), which then reports membershipprobabilities. Various other transfer functions and weight vectors wouldalso be suitable for use in this method.

The output of the neural network depicted in FIG. 14, or of analternative neural network or equivalent classifier, is the probabilitythat the gaming chip is of a particular denomination of authorizedgaming chips included in the set of chips on which the network wastrained, as described below.

FIG. 16 shows the output produced by the neural network of FIG. 14, whenpresented as input with the signature patterns of seven of the chipsfrom the example stack of FIG. 9. FIG. 16 includes three chips ofdenomination $5, two chips of denomination $1000, and two other chipsthat weren't included in the set of authorized chips used to train thenetwork.

If the signature pattern of the chip matches exactly one of theauthorized signatures within a specified tolerance, the software programrecords a chip of the corresponding denomination, and adds it to theamount of the bet.

If the signature pattern of the chip matches more than one of theauthorized signatures within a specified tolerance, the ABRS-IPPRnotifies the parent ABRS system that it is unable to determine the valueof a chip in the stack. This can occur if irregularity of lighting orshadow causes the system to incorrectly estimate the color of a band inthe chip signature. The ABRS can then signal the dealer to manuallyrecord the value of the bet. If this problem recurs, it may be necessaryto reduce the number or increase the distinctness of the authorizedchips at the table.

If the signature pattern of the chip fails to match any of theauthorized signatures within a specified tolerance, the ABRS-IPPRnotifies the parent ABRS system that an unauthorized chip is present inthe stack. The ABRS can then signal the dealer to manually verify theirregularity, and take appropriate action. Whereas the game deal maychoose to manually enter the amount of the wager into the system bymeans of a keypad as he/she would do in the event the players wager wascash or a call bet.

It will be readily understood by those skilled in the art that there areother arrangements and architectures of neural networks, and similarpattern-matching methods, which can similarly classify signaturepatterns into trained categories. It is understood that the presentinvention includes any and all such combinations of classificationmethods known to the art, and is not limited to the exemplary neuralnetwork architectures described above.

8. Report the Total Amount of the Wager

The total value of the player's bet is determined by summing the valueof each chip and denomination thereof in each wagered stack. TheABRS-IPPR reports the total value of the chips in the player's bettingarea to the parent ABRS system.

C. Training to Determining the Authorized Chip Patterns

The invention also teaches a method of determining the signaturepatterns of chips that are authorized for a particular table in acasino, and determining the matching tolerances for identifying players'chips as one of the authorized denominations. The ABRS can distinguishamong at least 12 different values (e.g. $1, $5, $25, $100, $500,$1,000, $5,000, $10,000, $25,000, $50,000 & $100,000) of authorized chipsignatures. The reliability of the ABRS-IPPR system is dependent on thenumber and distinctness of the chip signatures. When the number ofauthorized signatures is about one dozen, and the colors of bands in thesignatures are sufficiently distinct over the range of illumination inthe casino, the method of the invention is quite reliable. As the numberof authorized chip signatures increases, the method becomes less robust.

Prior to use of the system in any casino, the ABRS-IPPR is trained torecognize the chip signatures authorized for use at each gaming table.Such training is effected by capturing multiple images of stacks ofchips, including all authorized denominations, over a range ofillumination conditions representative of the conditions expected whenthe system is used. For each training stack imaged, the correctdenomination of each chip in the stack is supplied to the trainingsoftware program.

During training, the stack of chips of known denominations is imaged inthe same orientation and configuration as would be used fordetermination of the values of a stack of actual gaming chips in theexercise of the methods taught in this invention. The true value of thechips in the stack, and, optionally, the true colors of the bands ofsaid chips, are supplied to the training method. The training methoduses methods well known to the art, such as error back-propagation, toiteratively adjust the parameters of the neural network so as tooptimize the rate of correct classification of the chips in the trainingset.

During training, the relative size and the colors of enclosed symbolsimprinted within color bands of the chips are recorded and included inthe training set.

As a part of the training, the optimal color transformation of colorbands of the authorized chips, to be used in the flowchart 800, isdetermined. The optimal transformation is such as to maximize theseparation, in normalized color coordinates, of the chips in theauthorized set. Techniques for choosing transformations which maximizethe discrimination of exemplars in the training set are known to theart.

The number of training examples required to fully train the recognitionsystem depends on the number and distinctness of the authorized chipdenominations, and the variability of the illumination. In most cases,the number of training instances required will be between 100 and 10000.A sufficient number of training instances can be obtained quitereadily—for example, if two stacks, each containing 10 chips of a chipdenomination to be trained, were stacked in the betting area of each offive player positions at a gaming table, then a single image capturedfrom each player position would provide 100 training instances.

During training, the ABRS-IPPR learns the average signature of eachauthorized denomination of chip, the optimal color transformation to beemployed in flowchart 800 of the recognition method, and the optimalmatching tolerance to be used when matching the candidate chip signaturein the recognition method.

Increasing the number of training examples improves the ability of thesystem to correctly identify unauthorized chips. The training istypically non-linear, where the initial training has great impact andtraining in high volumes has an increasingly smaller impact, but whichmay nonetheless be worthwhile depending on the granularity of desireddetection.

If any new chip pattern is authorized for play, or if the pattern of anyauthorized chip changes, the system should be retrained. It may bedesirable to periodically retrain the system, even if there are few orno changes in authorized chip denominations or patterns, to protectagainst changes in the colors of different manufacturing lots ofnominally identical chips, and against changes in illumination at thecasino gaming tables.

D. Additional Applications

The invention is also applicable to other areas of a casino where itwould be useful to identify chips. For example, the invention can beimplemented in a cashier's booth to ensure that the correct amount ofmoney is given to patrons in exchange for their chips. A screen can beprovided visible to the cashier to inform the cashier of the correcttotal vale of the chips to be cashed in by the patron.

The invention can also be used in more than just card games, forexample, craps and other such games where the player has an area toplace a bet, the invention can identify the bet and assist the dealer inassessing the proper amount to pay out in the event of a player's win.In fact, since such games can include odds-related payouts requiring acalculation, the computer may be able to perform the calculation fasterand more accurately than a dealer.

E. Player's True Worth Computation

The invention can be used to automatically determine a player's trueworth to the casino. This computation is performed by the casinocomputer 150 using information regarding a player's wins and losses.Since the invention includes a player tracking card reader, theinvention communicates the identity of the player to the casino computeralong with the player's wins and losses. The casino then computes thevalue of the player to the casino and can provide the player withcomplementary goods and services based on the player's true worth.

As mentioned above, the invention can be used in combination with otherperipherals such as a card shoe of the type described in U.S. Pat. Nos.5,362,053; 5,374,061; 5,722,893; 6,039,650; 6,299,536 and 6,582,301 allincorporated herein by reference in their entirety. A card shoe of thistype provides one type of means for signaling the beginning of a game.In one aspect, the dealer presses a button on the card shoe 106, chiptray 120 or other location to signal the start of the game to thecomputer. In another aspect, the card shoe 106 can automatically signalthe computer 110 that a new game is beginning and can automaticallysignal the computer when the game is completed. This can be performedbecause the shoe tracks the cards dealt to the players and the dealer,and the shoe knows when a player wins and loses each hand and when thegame is over.

The invention can be employed in combination with any intelligent shoeto track the player's winnings and losses for the casino. This canassist the casino in determining a player's true worth to the casino.Moreover, since the invention includes a player tracking card reader,the invention can transmit data to the casino computer regarding theplayer's bet and wins and losses. This way, the casino can track theplayer's wins and losses at many games in the casino, and can accuratelydetermine the player's true worth.

F. Conclusion

The invention provides numerous aspects and advantages to a card gamewith automatic bet recognition. Advantages of the invention include theability to identify chips wagered by a player to automatically determinethe player's bet.

Having disclosed exemplary embodiments and the best mode, it will beunderstood by those skilled in the art that changes in form and detailmay be made therein without departing from the spirit and scope of theinvention.

1. A card delivery and bet recognition system comprising: a housingconfigured to store a plurality of playing cards and configured fordispensing cards to a number of players; a scanner configured toselectively scan the cards in the housing and to generate a scannersignal representative of the identity of each scanned card; at least onecamera positioned to view the bet location for each player and generatea bet signal representative of the bet for each player; and a processorcoupled to the scanner and each camera, and configured to process thescanner signal to identify each of the cards dispensed to each of theplayers playing the card game, and to process the image by locating atleast one chip and creating a signal representing the chip, comparingthe signal to a plurality of stored signatures, when a match occurs,generating a signal representing the bet.
 2. The card delivery and betrecognition system of claim 1, wherein: the player's bet includes aplurality of chips having at least two different denominations; and theimage processor is configured to identify edges of the chips, segmentthe image into a plurality of individual candidate chips, generate asignature for each of the candidate chips, identify each of thecandidate chips by comparing the signature of each candidate chip to aplurality of stored signatures representing valid chips and associateddenominations, and add denominations associated with each of the validchips to determine the wager.
 3. The card delivery and bet recognitionsystem of claim 1, further comprising: a switch coupled to the processorfor the dealer to indicate the start of a new game; and wherein theprocessor is configured to periodically store images from each cameraand to compare the images to one another, and when a change occursduring play of the game to generate an alarm signal.
 4. The carddelivery and bet recognition system of claim 2, further comprising: aswitch coupled to the processor for the dealer to indicate the start ofa new game; and wherein the processor is configured to periodicallystore images from each camera and to compare the images to one another,and when a change occurs during play of the game to generate an alarmsignal.
 5. The card delivery and bet recognition system of claim 1,further comprising: a central processor coupled to the processor, andconfigured to receive information regarding at least one player andcalculate a theoretical win of the casino; and wherein the centralprocessor is configured to generate a worth signal representative of theplayer's true worth.
 6. The card delivery and bet recognition system ofclaim 2, further comprising: a central processor coupled to theprocessor, and configured to receive information regarding at least oneplayer and calculate a theoretical win of the casino; and wherein thecentral processor is configured to generate a worth signalrepresentative of the player's true worth.
 7. The card delivery andplayer evaluation system of claim 1, wherein: the central processor isconfigured to generate a worth signal representative of the player'strue worth, and generate a comp value for the player.
 8. The carddelivery and player evaluation system of claim 1, further comprising: areader coupled to the processor and configured to read player trackingcards issued by a casino having information regarding the players. 9.The card delivery and player evaluation system of claim 1, furthercomprising: a keyboard coupled to the processor for the player to entergame-related information.
 10. The card delivery and player evaluationsystem of claim 1, further comprising: a chip tray image capture devicepositioned in proximity to a chip tray and configured to capture animage of the contents of the chip tray; and an image processor coupledto the chip tray image capture device and configured to process theimage by locating at least one chip and creating a signal representingthe chip, comparing the signal to a plurality of stored signatures, whena match occurs, generating a signal representing the contents of thechip tray.
 11. The card delivery and player evaluation system of claim10, further comprising: a reader coupled to the processor and configuredto read dealer tracking cards issued by a casino having informationregarding the dealers.